machine_learning/neuralNetwork.py

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+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+
+train_data_dir = "garbage_photos"
+
+input_shape = (150, 150, 3)
+num_classes = 5
+batch_size = 32
+epochs = 20
+
+train_datagen = ImageDataGenerator(rescale=1./255)
+
+train_generator = train_datagen.flow_from_directory(
+    train_data_dir,
+    target_size=(input_shape[0], input_shape[1]),
+    batch_size=batch_size,
+    class_mode='categorical'
+)
+
+model = Sequential()
+model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Conv2D(128, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Flatten())
+model.add(Dense(128, activation='relu'))
+model.add(Dense(num_classes, activation='softmax'))
+
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+model.fit(train_generator, epochs=epochs)
+
+classes = train_generator.class_indices
+
+model.save("neuralModel.h5")